The present invention relates to nonlinear processors, and in particular to an improved nonlinear processor for an echo canceller.
In a telephone network, 4-wire and 2-wire segments are joined at opposite ends of the network by hybrid circuits, often called 4:2 hybrids. Impedance mismatch in a hybrid circuit causes a 4-wire receive path signal to be reflected onto the 4-wire send path. If there is enough delay in the network, this reflected signal presents itself as echo to the speaker who originated it at the far end, and echo becomes more objectionable as both its level and delay are increased. Echo is one of the primary factors affecting the perceived quality of voice connections. Adaptive echo cancelers remove echo signals from a 4-wire end path.
Echo canceller applications have usually been associated with satellite or long terrestrial transmission paths, which introduce significant signal transmission delays. However, signal processing delays through new digital network elements, such as through the speech coding circuits for digitial cellular systems, are becoming a significant source of transmission delays and, therefore, of new echo canceller applications.
The continuing evolution of the worldwide telecommunications network from analog to digital transmission affects the performance expectations for echo cancelers. Digital facilities provide transmission quality that is relatively noise free. Such clean transmission, however, makes even minute impairments noticeable and potentially objectionable. Very low levels of residual echo, which previously went unnoticed, are now detectable and frequently found annoying. Callers who are often connected over low-noise, echo free digital facilities adjust their expectations, such that connections which were considered good several years ago may be rated poor today. Overall, the telecommunications network evolution and customer expectations are requiring expanded deployment of higher performance echo control devices.
Echo cancellers generally use an adaptive transversal filter structure capable of modeling a linear impulse response for an end path. Low level nonlinear distortion in the end path cannot be properly modeled and degrades echo canceller performance. This limits the echo canceller from removing all perceived echo from the circuit. For this reason, echo cancellers commonly employ a nonlinear processor (NLP), and after reasonable levels of cancellation are achieved, the characteristics of the NLP largely determine the perceived quality of the echo canceller performance. This is especially true in applications where the send out signal from the near end echo canceller is transmitted to the far end over low noise digital facilities.
Caller expectations regarding NLP characteristics have evolved considerably since wide deployment of echo cancellers began. Initially, callers familiar with echo suppressor characteristics were mainly concerned that the deficiencies of echo suppressors be adequately overcome by echo cancellers. These deficiencies involved the problems during double talk of either clipping the near end speech or passing excessive echo. As long as the double talk performance of an echo canceller was adequate, the single talk performance was expected to be at least as good as with an echo suppressor. Echo cancellers with a center clipper NLP transfer function generally met these expectations. For inputs below a suppression threshold, there was no output from the NLP. For inputs above the suppression threshold, the output equaled the input, with some distortion. The suppression threshold was generally adaptive and was designed to adjust to a level slightly above the expected residual echo level.
The center clipper NLP has a characteristic that some callers find objectionable. A "noise modulation" event occurs when the background noise from the near end facility is suppressed in response to far end speech during far end single talk. Because the background signal was primarily Gaussian noise, a "noise matching" feature was added to some echo cancellers to mask this phenomenon. A noise matching feature as applied to an echo canceller NLP operates in such a manner that the near end background noise level is estimated on the send in port. Pseudo-random noise at approximately the estimated background noise level is then injected onto the send out port to the far end whenever the NLP is activated. In the evolving telecommunications networks of late, however, noisy analog and FDM carrier systems in the near end circuits are rapidly giving way to digital optical fiber and circuits with low idle channel noise. In this environment, the background signals modulated by the NLP usually consist of environmental sounds such as people talking, a computer printer, music, etc. In these cases, replacing the background sounds with pseudorandom noise is nearly as obtrusive as inserting silence. To be as unobtrusive as possible, an NLP needs to match not only the near end background noise levels, but also their statistical properties and spectral content. Noise matching cannot achieve this.